Globally Convergent Interior-Point Algorithm for Nonlinear Programming

I. Akrotirianakis and B. Rustem
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I. Akrotirianakis: Princeton University
B. Rustem: Imperial College

Journal of Optimization Theory and Applications, 2005, vol. 125, issue 3, No 1, 497-521

Abstract: Abstract This paper presents a primal-dual interior-point algorithm for solving general constrained nonlinear programming problems. The inequality constraints are incorporated into the objective function by means of a logarithmic barrier function. Also, satisfaction of the equality constraints is enforced through the use of an adaptive quadratic penalty function. The penalty parameter is determined using a strategy that ensures a descent property for a merit function. Global convergence of the algorithm is achieved through the monotonic decrease of a merit function. Finally, extensive computational results show that the algorithm can solve large and difficult problems in an efficient and robust way.

Keywords: Primal-dual interior-point algorithms; merit functions; convergence theory (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10957-005-2086-2

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